Integrative Multi-Omic Variant Analysis Framework
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Solution Overview
Problem
Current methods for analyzing multi-omic data fail to effectively characterize the functional impact of genomic variants by not considering inter-relationships across different data modalities, leading to a lack of insights into the functional or pathological impacts of individual aberrations.
Innovation Solution
A system and method that determine various statuses including mutation, splice variant, variant-based expression regulation, gene-based expression regulation, and gene-based CNV and epigenetic impact statuses, adjusting these statuses to produce a final list of variants and associated information, which are filtered and ranked for functional impact analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multi-omic data are analyzed in separate silos by different bioinformatics pipelines, then the analysis process is simple and manageable, but the inter-relationships across data modalities cannot be utilized to build evidence for the functional impact of genomic variants
Solution Approach 1:
The patent merges multiple separate bioinformatics pipelines into a unified integrative framework that simultaneously analyzes genomic, transcriptomic, proteomic, and epigenomic data. This integration allows the system to capture inter-relationships across data modalities and build comprehensive evidence for functional variant impacts, directly resolving the contradiction between information completeness and system complexity.
Solution Approach 2:
The patent creates a universal analysis framework that handles multiple -omic data types through a single integrated system. The framework provides multi-functional capabilities to process diverse data modalities (genomic, transcriptomic, proteomic, epigenomic) while maintaining the ability to analyze inter-relationships, thereby preventing information loss without requiring separate specialized pipelines for each data type.
2Loss of information
If multiple types of -omic data are generated for the same sample, then the understanding of the biological system is improved, but the complexity of data analysis increases
Solution Approach 1:
The patent combines multiple -omic data types (genomic, transcriptomic, proteomic, epigenomic) into a unified analysis framework that processes them simultaneously. This merging approach maintains comprehensive biological system understanding while managing complexity through integration rather than through multiple separate analysis pipelines.
Solution Approach 2:
The patent segments the complex multi-omic analysis into distinct functional modules that handle different data types and analysis tasks. Each module processes specific -omic data while the overall framework integrates results, making the complex analysis manageable through structured segmentation of the analytical process.
3Loss of information
If separate bioinformatics pipelines are used for different -omic data, then the analysis process is easier to implement, but new insights into the functional or pathological impacts of individual aberrations cannot be generated
Solution Approach 1:
The patent merges separate bioinformatics pipelines into a unified system that generates new functional and pathological insights by analyzing inter-relationships across data modalities. The integration enables discovery of insights that would be impossible to obtain through separate pipeline analysis, while the modular architecture maintains ease of implementation.
Solution Approach 2:
The patent introduces an intermediary integrative framework that connects different -omic data analysis processes. This intermediary layer coordinates the analysis of multiple data types and synthesizes results to generate new insights, bridging the gap between simple separate pipeline analysis and complex integrated analysis.
Data Source
AI summary
A method (100) for characterizing a functional impact of a plurality of variants, comprising: obtaining (110) information comprising at least a plurality of variants, gene expression information, copy number variation, and epigenetic effects; determining (120) a splice status for the variant; determining (130) a variant-based expression regulation status, comprising whether the variant has an effect on gene expression; determining (140) a gene-based expression regulation status, comprising an indication of whether the variant has a functional impact on a target gene; determining (150) a gene-based copy number variant (CNV) and epigenetic impact status, comprising whether one or both has an impact on expression of a gene; adjusting (160), based on the CNV and epigenetic impact status, the variant-based and/or the gene-based expression regulation status; and reporting (170) at least the adjusted variant-based and/or the adjusted gene-based expression regulation status for each of a plurality of variants and/or genes from the genomic sample.